Master Python Programming (From Basic to Data Science)

Course Title: Mastering Python Programming – From Basics to Data Science

Syllabus:

Module 1: Python Fundamentals

1. Introduction to Python

   • History and evolution of Python

   • Features and real-world applications

2. Installation of Python

   • Installing Python on various OS

   • Setting up environment variables

   • Installing IDEs (PyCharm, VS Code)

3. Jupyter Notebook Overview

   • Installing Jupyter

   • Using cells, markdown, and basic operations

   • Code execution and visualization

4. Variables, Keywords and Comments

   • Declaring and initializing variables

   • Python keywords and naming conventions

   • Writing comments and docstrings

5. Operators in Python

   • Arithmetic, relational, logical, bitwise, assignment, and special operators

   • Operator precedence

6. How to Take Input from User

   • input() and print() functions

   • Formatting output using format() and f-strings

Module 2: Control Flow and Data Types

7. Conditional Statements

   • if, else, elif

   • Nested conditions and shorthand syntax

8. Looping Statements

   • for, while loops

   • break, continue, and pass statements

9. Data Types in Python

   • Numbers, strings, lists, tuples, dictionaries, and sets

   • Type casting and type checking

Module 3: Functions and OOPs

10. Functions in Python

    • Defining and calling functions

    • Arguments, return values, and scope

    • *args and **kwargs

11. Lambda Functions

    • Syntax and use cases

    • Anonymous functions in Pythonic code

12. Decorators and Generators

    • Writing and applying decorators

    • yield, next() for generator functions

13. Classes and Objects

    • Basics of OOP

    • __init__() method and instance variables

14. OOPS Concept

    • Inheritance, polymorphism, encapsulation, and abstraction

    • Method overriding and super()

Module 4: File and Error Handling

15. File Handling and Exception Handling

    • Reading and writing files

    • File modes and context manager (with)

    • try, except, finally, raise, and custom exceptions

16. Regular Expressions

    • re module

    • Pattern matching, searching, and replacing

17. Logging and Debugging in Python

    • Using logging module

    • Setting log levels

    • Debugging techniques

18. Python Testing

    • Unit testing with unittest

    • Writing test cases and test suites

19. Command Line Arguments

    • Using sys.argv

    • argparse module for advanced parsing

Module 5: Advanced Python and Data Science

20. Databases in Python

    • Connecting with SQLite and MySQL

    • Executing queries using Python

21. API Development in Python

    • Basics of REST APIs

    • Using Flask to build simple APIs

22. Pydantic (Data Validation Framework)

    • Data models and schema validation

    • Type hints and data parsing

23. Python Libraries for Data Science

    • Overview of NumPy, Pandas, Matplotlib, Seaborn

    • Basic operations and data visualization

24. End to End Project on Data Science

    • Project problem statement

    • Data preprocessing, analysis, modeling, and visualization

    • Model evaluation and final report

25. Important Concepts That Everyone Should Know

    • Best practices in Python

    • Code optimization and readability

    • Preparing for interviews and real-world applications